There are various settings for zero-shot semantic segmentation due to the differences in evaluation protocols or data split manner. Among them, the most widely used setting [1]}, [2]}, [3]} is Generalized Zero-shot Semantic Segmentation. In this setting, a training set \({\cal X}_{\mathrm {train}}=\left\lbrace  ({\cal I}_{k},{\cal M}_{k})\right\rbrace \)  with input images \({\cal I}_{k}\)  and the ground-truth semantic segmentation annotations \({\cal M}_{k}\)  are provided for model training, and a testing set \({\cal X}_{\mathrm {test}}\)  are used for evaluation. In particular, the annotations {\({\cal M}_{k}\) } of the training set \({\cal X}_{\mathrm {train}}\)  contains only the seen classes, while both seen classes and unseen classes need to be predicted in testing set \({\cal X}_{\mathrm {test}}\) . In this work, we target this setting for evaluating our method.
